Mini-Project 02
Data Visualization Project 02
Initial Plans
I was initially planning to explore the Florida Lakes spatial dataset more. I wanted to highlight the counties with the most area of lakes. I found it hard to try to merge the geoms and find the intersections to get this to work. I also wanted to try to show the correlation of house prices to certain futures of houses, I figured scatter plots would be the best way to visualize this.
Narrative
We load in all of the different packages and data for our analysis in this first chunk. Sadley nothing too fun here…
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library("maps")##
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## mapstates <- st_as_sf(map("state", plot = FALSE, fill = TRUE))
counties <- st_as_sf(map("county", plot = FALSE, fill = TRUE))
setwd( "..")
florida_lakes <- read_sf("data/Florida_Lakes/Florida_Lakes.shp")
houses <- read_csv("data/WestRoxbury.csv")## Rows: 5802 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): REMODEL
## dbl (13): TOTAL VALUE, TAX, LOT SQFT, YR BUILT, GROSS AREA, LIVING AREA, FLO...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Next, we can begin to take a look at the houses dataset. I found it very interesting that the largest average house sizes per year were in the 1800s. I would like to dive more into this in the future.
houses %>%
group_by(`YR BUILT`) %>%
summarise(Average_Size = mean(`LIVING AREA`, na.rm = TRUE),
Average_Value = min(`TOTAL VALUE`, na.rm = TRUE),
Average_Users_Rooms = max(ROOMS, na.rm = TRUE)) %>%
arrange(desc(Average_Size))## # A tibble: 149 × 4
## `YR BUILT` Average_Size Average_Value Average_Users_Rooms
## <dbl> <dbl> <dbl> <dbl>
## 1 1881 3653 737. 11
## 2 1851 3527 705. 9
## 3 1848 3168 689. 12
## 4 1884 3039. 418. 12
## 5 1798 2953 438. 11
## 6 2010 2952. 534 9
## 7 1976 2944 398. 9
## 8 2011 2885. 458. 10
## 9 1874 2884 714. 10
## 10 1998 2806 365. 9
## # ℹ 139 more rowsBelow is an interactive plot that you can use to look at the houses dataset comparing the total value of a house to its living area. Feel free to explore it and take a look at some of the outliers.
plot <- ggplot(houses, aes(x = `LIVING AREA`, y = `TOTAL VALUE`, size = BEDROOMS, color = BEDROOMS)) +
geom_point(alpha = 0.7) +
labs(title = "Total Value vs Living Area",
x = "Living Area (sqft)",
y = "Total Value (in $1000s)",
size = "Number of Bedrooms",
color = "Number of Bedrooms") +
scale_size_continuous(range = c(.1, 3)) +
theme_minimal()
# Convert ggplot2 to ggplotly for interactivity
inter_plot <- ggplotly(plot)
inter_plothtmlwidgets::saveWidget(inter_plot, "value_vs_area.html")Next we move to the Florida lakes data, we can see how lakes are spread through different counties. I limited this to central Florida so we can see lakes we are familiar with and pointed out the largest one.
invalid_geometries <- !st_is_valid(florida_lakes)
florida_lakes <- st_make_valid(florida_lakes)
florida_lakes <- florida_lakes %>%
mutate(area = st_area(geometry)) %>%
arrange(desc(area))
florida_lakes$area <- as.numeric(florida_lakes$area)
lake_summary <- florida_lakes %>%
group_by(COUNTY) %>%
summarise(Average_Area = mean(area, na.rm = TRUE),
total_lakes = n(),
total_area = sum(area, na.rm = TRUE),
mean_area = mean(area, na.rm = TRUE),
median_area = median(area, na.rm = TRUE),
max_area = max(area, na.rm = TRUE),
min_area = min(area, na.rm = TRUE)) %>%
arrange(desc(Average_Area))
lake_summary## Simple feature collection with 67 features and 8 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: -87.42774 ymin: 25.02625 xmax: -80.03097 ymax: 31.00254
## Geodetic CRS: WGS 84
## # A tibble: 67 × 9
## COUNTY Average_Area total_lakes total_area mean_area median_area max_area
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 PALM BEACH 70315565. 19 1.34e9 70315565. 239690. 1.30e9
## 2 INDIAN RI… 13515194. 2 2.70e7 13515194. 13515194. 2.70e7
## 3 OSCEOLA 6920741. 56 3.88e8 6920741. 818668. 1.27e8
## 4 GULF 2767325. 11 3.04e7 2767325. 76240. 1.63e7
## 5 HIGHLANDS 2463776. 78 1.92e8 2463776. 277742. 1.07e8
## 6 BAKER 2436310. 3 7.31e6 2436310. 1649. 7.31e6
## 7 CITRUS 2222842. 27 6.00e7 2222842. 155148. 3.26e7
## 8 ALACHUA 2061077. 68 1.40e8 2061077. 175969. 2.95e7
## 9 UNION 2031957. 4 8.13e6 2031957. 2066163. 3.74e6
## 10 MONROE 2026182. 26 5.27e7 2026182. 214442. 1.54e7
## # ℹ 57 more rows
## # ℹ 2 more variables: min_area <dbl>, geometry <MULTIPOLYGON [°]>largest_lake <- florida_lakes[1, ]
centroid <- st_centroid(largest_lake$geometry)
label <- largest_lake$NAME
x_shift <- -.7
y_shift <- -.3
ggplot(data = florida_lakes) +
geom_sf(data = states) +
geom_sf(data = counties) +
geom_sf(aes(fill = area)) +
scale_fill_viridis_c(option = "plasma",name = "Area (sq units)") +
coord_sf(xlim = c(-83, -80), ylim = c(26.5, 29), expand = FALSE) +
ggtitle("Map of Central Florida Lakes by Area") +
theme_minimal() +
annotate("text", x = st_coordinates(centroid)[, "X"] + x_shift ,
y = st_coordinates(centroid)[, "Y"] + y_shift,
label = label, color = "red", size = 5, hjust = 0)
Below is the houses dataset again mapped with a linear model
correlation. Much to our expectation we can visualize that house size
has large correlation to the price.
model <- lm(`TOTAL VALUE` ~ `LIVING AREA` + ROOMS + BEDROOMS, data = houses)
houses <- houses %>%
mutate(predicted_value = predict(model, houses),
residuals = `TOTAL VALUE` - predicted_value)
ggplot(houses, aes(x = `LIVING AREA`, y = `TOTAL VALUE`)) +
geom_point(aes(color = residuals), alpha = 0.6) + # Color points by residuals
geom_smooth(method = "lm", formula = y ~ x, color = "blue") +
labs(title = "Prediction of House Total Value Based on Living Area and Number of Rooms",
x = "Living Area (sq ft)",
y = "Total Value ($)",
subtitle = "A multiple linear regression model to predict house value",
caption = "Data Source: [Your Data Source]") +
theme_minimal() +
scale_color_gradient(low = "red", high = "green") + # Residuals color gradient
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
This is then backed up by the below correlation coefficients.
house_model <- lm(`LIVING AREA` ~ `TOTAL VALUE`, houses)
houses_coefs <- tidy(house_model, conf.int = TRUE) %>%
filter(term != "(Intercept)")
houses_coefs## # A tibble: 1 × 7
## term estimate std.error statistic p.value conf.low conf.high
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 `TOTAL VALUE` 4.56 0.0391 117. 0 4.49 4.64Overall, this project should highlight some of the basic ways to look at data. While this is just the surface these visualizations could be improved upon to add in more factors. I found the spatial data difficult to work with and would like to dive into this more. I find it to be a very interesting topic as well.